Bayesian hierarchical finite mixture of regression for histopathological imaging-based cancer data analysis

Yunju Im; Yuan Huang; Jian Huang; Shuangge Ma

doi:10.1002/sim.9309

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Bayesian hierarchical finite mixture of regression for histopathological imaging-based cancer data analysis

Journal article

Open access

Peer reviewed

Bayesian hierarchical finite mixture of regression for histopathological imaging-based cancer data analysis

Yunju Im, Yuan Huang, Jian Huang and Shuangge Ma

Statistics in medicine, Vol.41(6), pp.1009-1022

03/15/2022

DOI: 10.1002/sim.9309

PMCID: PMC8881390

PMID: 35028949

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https://www.ncbi.nlm.nih.gov/pmc/articles/8881390View

Open Access

Abstract

Cancer is heterogeneous, and for seemingly similar cancer patients, the associations between an outcome/phenotype and covariates can be different. To describe such differences, finite mixture of regression (FMR) and other modeling techniques have been developed. "Classic" FMR analysis has usually been based on clinical, demographic, and molecular variables. More recently, histopathological imaging data-which is a byproduct of biopsy and enjoys broader data availability and higher cost-effectiveness-has been increasingly used in cancer modeling, although it is noted that its application to cancer FMR analysis still remains limited. In this article, we further advance cancer FMR analysis based on histopathological imaging data. Significantly advancing from the existing analyses under heterogeneity and homogeneity, our goal is to simultaneously use two types of histopathological imaging features, which are extracted based on domain-specific biomedical knowledge and using automated signal processing software, respectively. A significant modeling/methodological advancement is that, to reflect the "increased resolution" of the second type of imaging features over the first type, we impose a hierarchy in the mixture structures. An effective and flexible Bayesian approach is proposed. Simulation shows its competitiveness over several alternatives. The TCGA lung cancer data is analyzed, and interesting heterogeneous structures different from using the alternatives are found. Overall, this study provides a new venue for FMR analysis for cancer and other complex diseases.

Bayes Theorem

Computer Simulation

Data Analysis

Humans

Neoplasms - diagnostic imaging

Regression Analysis

Details

Title: Subtitle: Bayesian hierarchical finite mixture of regression for histopathological imaging-based cancer data analysis
Creators: Yunju Im - Yale University
Yuan Huang - Yale University
Jian Huang - University of Iowa
Shuangge Ma - Yale University
Resource Type: Journal article
Publication Details: Statistics in medicine, Vol.41(6), pp.1009-1022
DOI: 10.1002/sim.9309
PMID: 35028949
PMCID: PMC8881390
NLM abbreviation: Stat Med
ISSN: 0277-6715
eISSN: 1097-0258
Grant note: P50 CA196530 / NCI NIH HHS R03 CA241699 / NCI NIH HHS R01 CA204120 / NCI NIH HHS
Language: English
Date published: 03/15/2022
Academic Unit: Statistics and Actuarial Science; Biostatistics
Record Identifier: 9984257594902771

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